[2024-03-06 15:23:12,455] INFO: Will use torch.nn.parallel.DistributedDataParallel() and 4 gpus [2024-03-06 15:23:12,457] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 15:23:12,457] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 15:23:12,457] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 15:23:12,457] INFO: NVIDIA GeForce GTX 1080 Ti [2024-03-06 15:23:21,573] INFO: using attention_type=efficient [2024-03-06 15:23:21,579] INFO: using attention_type=efficient [2024-03-06 15:23:21,584] INFO: using attention_type=efficient [2024-03-06 15:23:21,590] INFO: using attention_type=efficient [2024-03-06 15:23:21,596] INFO: using attention_type=efficient [2024-03-06 15:23:21,601] INFO: using attention_type=efficient [2024-03-06 15:23:24,570] INFO: DistributedDataParallel( (module): MLPF( (nn0): Sequential( (0): Linear(in_features=42, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=256, bias=True) ) (conv_id): ModuleList( (0-2): 3 x SelfAttentionLayer( (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (norm0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (seq): Sequential( (0): Linear(in_features=256, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): Linear(in_features=256, out_features=256, bias=True) (3): ELU(alpha=1.0) ) (dropout): Dropout(p=0.3, inplace=False) ) ) (conv_reg): ModuleList( (0-2): 3 x SelfAttentionLayer( (mha): MultiheadAttention( (out_proj): NonDynamicallyQuantizableLinear(in_features=256, out_features=256, bias=True) ) (norm0): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (norm1): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (seq): Sequential( (0): Linear(in_features=256, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): Linear(in_features=256, out_features=256, bias=True) (3): ELU(alpha=1.0) ) (dropout): Dropout(p=0.3, inplace=False) ) ) (nn_id): Sequential( (0): Linear(in_features=810, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=9, bias=True) ) (nn_pt): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_eta): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_sin_phi): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_cos_phi): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_energy): RegressionOutput( (nn): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=2, bias=True) ) ) (nn_charge): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=3, bias=True) ) (nn_probX): Sequential( (0): Linear(in_features=819, out_features=256, bias=True) (1): ELU(alpha=1.0) (2): LayerNorm((256,), eps=1e-05, elementwise_affine=True) (3): Dropout(p=0.3, inplace=False) (4): Linear(in_features=256, out_features=1, bias=True) ) ) ) [2024-03-06 15:23:24,572] INFO: Trainable parameters: 4139031 [2024-03-06 15:23:24,572] INFO: Non-trainable parameters: 0 [2024-03-06 15:23:24,572] INFO: Total parameters: 4139031 [2024-03-06 15:23:24,586] INFO: Modules Trainable params Non-tranable params Trainable Parameters Non-tranable Parameters module.nn0.0.weight NaN NaN 10752.0 - module.nn0.0.bias NaN NaN 256.0 - module.nn0.2.weight NaN NaN 256.0 - module.nn0.2.bias NaN NaN 256.0 - module.nn0.4.weight NaN NaN 65536.0 - module.nn0.4.bias NaN NaN 256.0 - module.conv_id.0.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.0.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.0.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.0.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.0.norm0.weight NaN NaN 256.0 - module.conv_id.0.norm0.bias NaN NaN 256.0 - module.conv_id.0.norm1.weight NaN NaN 256.0 - module.conv_id.0.norm1.bias NaN NaN 256.0 - module.conv_id.0.seq.0.weight NaN NaN 65536.0 - module.conv_id.0.seq.0.bias NaN NaN 256.0 - module.conv_id.0.seq.2.weight NaN NaN 65536.0 - module.conv_id.0.seq.2.bias NaN NaN 256.0 - module.conv_id.1.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.1.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.1.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.1.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.1.norm0.weight NaN NaN 256.0 - module.conv_id.1.norm0.bias NaN NaN 256.0 - module.conv_id.1.norm1.weight NaN NaN 256.0 - module.conv_id.1.norm1.bias NaN NaN 256.0 - module.conv_id.1.seq.0.weight NaN NaN 65536.0 - module.conv_id.1.seq.0.bias NaN NaN 256.0 - module.conv_id.1.seq.2.weight NaN NaN 65536.0 - module.conv_id.1.seq.2.bias NaN NaN 256.0 - module.conv_id.2.mha.in_proj_weight NaN NaN 196608.0 - module.conv_id.2.mha.in_proj_bias NaN NaN 768.0 - module.conv_id.2.mha.out_proj.weight NaN NaN 65536.0 - module.conv_id.2.mha.out_proj.bias NaN NaN 256.0 - module.conv_id.2.norm0.weight NaN NaN 256.0 - module.conv_id.2.norm0.bias NaN NaN 256.0 - module.conv_id.2.norm1.weight NaN NaN 256.0 - module.conv_id.2.norm1.bias NaN NaN 256.0 - module.conv_id.2.seq.0.weight NaN NaN 65536.0 - module.conv_id.2.seq.0.bias NaN NaN 256.0 - module.conv_id.2.seq.2.weight NaN NaN 65536.0 - module.conv_id.2.seq.2.bias NaN NaN 256.0 - module.conv_reg.0.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.0.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.0.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.0.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.0.norm0.weight NaN NaN 256.0 - module.conv_reg.0.norm0.bias NaN NaN 256.0 - module.conv_reg.0.norm1.weight NaN NaN 256.0 - module.conv_reg.0.norm1.bias NaN NaN 256.0 - module.conv_reg.0.seq.0.weight NaN NaN 65536.0 - module.conv_reg.0.seq.0.bias NaN NaN 256.0 - module.conv_reg.0.seq.2.weight NaN NaN 65536.0 - module.conv_reg.0.seq.2.bias NaN NaN 256.0 - module.conv_reg.1.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.1.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.1.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.1.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.1.norm0.weight NaN NaN 256.0 - module.conv_reg.1.norm0.bias NaN NaN 256.0 - module.conv_reg.1.norm1.weight NaN NaN 256.0 - module.conv_reg.1.norm1.bias NaN NaN 256.0 - module.conv_reg.1.seq.0.weight NaN NaN 65536.0 - module.conv_reg.1.seq.0.bias NaN NaN 256.0 - module.conv_reg.1.seq.2.weight NaN NaN 65536.0 - module.conv_reg.1.seq.2.bias NaN NaN 256.0 - module.conv_reg.2.mha.in_proj_weight NaN NaN 196608.0 - module.conv_reg.2.mha.in_proj_bias NaN NaN 768.0 - module.conv_reg.2.mha.out_proj.weight NaN NaN 65536.0 - module.conv_reg.2.mha.out_proj.bias NaN NaN 256.0 - module.conv_reg.2.norm0.weight NaN NaN 256.0 - module.conv_reg.2.norm0.bias NaN NaN 256.0 - module.conv_reg.2.norm1.weight NaN NaN 256.0 - module.conv_reg.2.norm1.bias NaN NaN 256.0 - module.conv_reg.2.seq.0.weight NaN NaN 65536.0 - module.conv_reg.2.seq.0.bias NaN NaN 256.0 - module.conv_reg.2.seq.2.weight NaN NaN 65536.0 - module.conv_reg.2.seq.2.bias NaN NaN 256.0 - module.nn_id.0.weight NaN NaN 207360.0 - module.nn_id.0.bias NaN NaN 256.0 - module.nn_id.2.weight NaN NaN 256.0 - module.nn_id.2.bias NaN NaN 256.0 - module.nn_id.4.weight NaN NaN 2304.0 - module.nn_id.4.bias NaN NaN 9.0 - module.nn_pt.nn.0.weight NaN NaN 209664.0 - module.nn_pt.nn.0.bias NaN NaN 256.0 - module.nn_pt.nn.2.weight NaN NaN 256.0 - module.nn_pt.nn.2.bias NaN NaN 256.0 - module.nn_pt.nn.4.weight NaN NaN 512.0 - module.nn_pt.nn.4.bias NaN NaN 2.0 - module.nn_eta.nn.0.weight NaN NaN 209664.0 - module.nn_eta.nn.0.bias NaN NaN 256.0 - module.nn_eta.nn.2.weight NaN NaN 256.0 - module.nn_eta.nn.2.bias NaN NaN 256.0 - module.nn_eta.nn.4.weight NaN NaN 512.0 - module.nn_eta.nn.4.bias NaN NaN 2.0 - module.nn_sin_phi.nn.0.weight NaN NaN 209664.0 - module.nn_sin_phi.nn.0.bias NaN NaN 256.0 - module.nn_sin_phi.nn.2.weight NaN NaN 256.0 - module.nn_sin_phi.nn.2.bias NaN NaN 256.0 - module.nn_sin_phi.nn.4.weight NaN NaN 512.0 - module.nn_sin_phi.nn.4.bias NaN NaN 2.0 - module.nn_cos_phi.nn.0.weight NaN NaN 209664.0 - module.nn_cos_phi.nn.0.bias NaN NaN 256.0 - module.nn_cos_phi.nn.2.weight NaN NaN 256.0 - module.nn_cos_phi.nn.2.bias NaN NaN 256.0 - module.nn_cos_phi.nn.4.weight NaN NaN 512.0 - module.nn_cos_phi.nn.4.bias NaN NaN 2.0 - module.nn_energy.nn.0.weight NaN NaN 209664.0 - module.nn_energy.nn.0.bias NaN NaN 256.0 - module.nn_energy.nn.2.weight NaN NaN 256.0 - module.nn_energy.nn.2.bias NaN NaN 256.0 - module.nn_energy.nn.4.weight NaN NaN 512.0 - module.nn_energy.nn.4.bias NaN NaN 2.0 - module.nn_charge.0.weight NaN NaN 209664.0 - module.nn_charge.0.bias NaN NaN 256.0 - module.nn_charge.2.weight NaN NaN 256.0 - module.nn_charge.2.bias NaN NaN 256.0 - module.nn_charge.4.weight NaN NaN 768.0 - module.nn_charge.4.bias NaN NaN 3.0 - module.nn_probX.0.weight NaN NaN 209664.0 - module.nn_probX.0.bias NaN NaN 256.0 - module.nn_probX.2.weight NaN NaN 256.0 - module.nn_probX.2.bias NaN NaN 256.0 - module.nn_probX.4.weight NaN NaN 256.0 - module.nn_probX.4.bias NaN NaN 1.0 - [2024-03-06 15:23:24,609] INFO: Creating experiment dir /pfvol/experiments/MLPF_cms_Transformer_MET_True_pyg-cms-ttbar_20240306_152311_822352 [2024-03-06 15:23:24,610] INFO: Model directory /pfvol/experiments/MLPF_cms_Transformer_MET_True_pyg-cms-ttbar_20240306_152311_822352 [2024-03-06 15:23:25,069] INFO: train_dataset: cms_pf_ttbar, 80000 [2024-03-06 15:23:25,130] INFO: valid_dataset: cms_pf_ttbar, 20000 [2024-03-06 15:23:25,189] INFO: Initiating epoch #1 train run on device rank=0 [2024-03-06 19:17:51,212] INFO: Initiating epoch #1 valid run on device rank=0 [2024-03-06 19:33:15,234] INFO: Rank 0: epoch=1 / 30 train_loss=89.0129 valid_loss=80.9067 stale=0 time=249.83m eta=7245.2m [2024-03-06 19:33:15,396] INFO: Initiating epoch #2 train run on device rank=0 [2024-03-06 23:27:30,971] INFO: Initiating epoch #2 valid run on device rank=0 [2024-03-06 23:40:49,463] INFO: Rank 0: epoch=2 / 30 train_loss=79.8452 valid_loss=80.1976 stale=0 time=247.57m eta=6963.7m [2024-03-06 23:40:50,724] INFO: Initiating epoch #3 train run on device rank=0 [2024-03-07 03:35:34,255] INFO: Initiating epoch #3 valid run on device rank=0 [2024-03-07 03:51:08,029] INFO: Rank 0: epoch=3 / 30 train_loss=78.9178 valid_loss=80.2062 stale=1 time=250.29m eta=6729.4m [2024-03-07 03:51:09,056] INFO: Initiating epoch #4 train run on device rank=0 [2024-03-07 07:45:11,204] INFO: Initiating epoch #4 valid run on device rank=0 [2024-03-07 07:57:24,447] INFO: Rank 0: epoch=4 / 30 train_loss=78.5646 valid_loss=79.2375 stale=0 time=246.26m eta=6460.9m [2024-03-07 07:57:25,600] INFO: Initiating epoch #5 train run on device rank=0 [2024-03-07 11:50:43,809] INFO: Initiating epoch #5 valid run on device rank=0 [2024-03-07 12:04:12,052] INFO: Rank 0: epoch=5 / 30 train_loss=78.3849 valid_loss=79.6252 stale=1 time=246.77m eta=6203.9m [2024-03-07 12:04:12,978] INFO: Initiating epoch #6 train run on device rank=0